ICA Based Super-Resolution Face Hallucination and Recognition

Authors:
Hua Yan;Ju Liu;Jiande Sun;Xinghua Sun
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, 250100, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, 250100, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, 250100, Shandong, China;School of Information Science and Engineering, Shandong University, Jinan, 250100, Shandong, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

Citing 4
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Abstract

In this paper, we propose a new super-resolution face hallucination and recognition method based on Independent Component Analysis (ICA). Firstly, ICA is used to build a linear mixing relationship between high-resolution (HR) face image and independent HR source faces images. The linear mixing coefficients are retained, thus the corresponding low-resolution (LR) face image is represented by linear mixture of down-sampled source faces images. So, when the source faces images are obtained by training a set of HR face images, unconstrained least square is utilized to obtain mixing coefficients to a LR image for hallucination and recognition. Experiments show that the accuracy of face recognition is insensitive to image size and the number of HR source faces images when image size is larger than 8×8, and the resolution and quality of the hallucinated face image are greatly enhanced over the LR ones, which is very helpful for human recognition.